As of early 2023, approximately 43 percent of consumers in the United States said they would prefer to shop mostly online rather than in-store, making it the country with highest online shopping preference. In contrast, more shoppers preferred visiting physical stores in countries such as Austria, Finland, and New Zealand.

In 2024, global retail e-commerce sales reached an estimated ************ U.S. dollars. Projections indicate a ** percent growth in this figure over the coming years, with expectations to come close to ************** dollars by 2028. World players Among the key players on the world stage, the American marketplace giant Amazon holds the title of the largest e-commerce player globally, with a gross merchandise value of nearly *********** U.S. dollars in 2024. Amazon was also the most valuable retail brand globally, followed by mostly American competitors such as Walmart and the Home Depot. Leading e-tailing regions E-commerce is a dormant channel globally, but nowhere has it been as successful as in Asia. In 2024, the e-commerce revenue in that continent alone was measured at nearly ************ U.S. dollars, outperforming the Americas and Europe. That year, the up-and-coming e-commerce markets also centered around Asia. The Philippines and India stood out as the swiftest-growing e-commerce markets based on online sales, anticipating a growth rate surpassing ** percent.

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

Photo by CardMapr on Unsplash

This is a dataset obtained from an online survey conducted in August 2020.

In the survey, participants were introduced to the concept of a smartphone-based shopping assistant application with the help of pictures and videos when shopping with and without the application. Participants were presented with three different shopping scenarios. In each scenario, we showed products on a shelf (groceries, luxury chocolate, shoes, books). The first shopping scenario was a regular shopping scenario (RSS), the second was an augmented reality shopping scenario (ARSS), and the third was an augmented reality shopping scenario with explainable AI features (XARSS). For each scenario participants had to answer questions about how they perceived the scenario and how it influenced their overall purchase intention.

The present work was conducted within the Innovative Training Network project PERFORM funded by the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 765395. The EU Research Executive Agency is not responsible for any use that may be made of the information it contains.

A comprehensive dataset providing key insights into the eCommerce industry, including global retail online sales projections, number of eCommerce stores, digital buyer statistics, revenue growth in the United States, sector-wise revenue details with a focus on consumer electronics, average conversion rates, and mobile commerce sales forecasts.

China Online Retail Sales: YoY: Year to Date: Goods and Service data was reported at 7.900 % in Mar 2025. This records an increase from the previous number of 7.300 % for Feb 2025. China Online Retail Sales: YoY: Year to Date: Goods and Service data is updated monthly, averaging 17.100 % from Feb 2015 (Median) to Mar 2025, with 112 observations. The data reached an all-time high of 44.600 % in Feb 2015 and a record low of -3.000 % in Feb 2020. China Online Retail Sales: YoY: Year to Date: Goods and Service data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HA: Online Retail Sales.

Context RedFlagDeals is a forum where users can post product sales that they come across. The "All Hot Deals" section of the forum was scraped for relevant information on July 17, 2020.

I supplied a kernel on how to clean the data and will follow up with some analyses for identifying promising deals. I will continue updating the data-set with new posts on the forum should there be sufficient interest, wich I will evaluate based on the number of downloads and upvotes.

Content Three tables are supplied.

Each row in the main table corresponds to a post. Columns indicate post information such as the title, the sum of up-votes minus down-votes, a link to the referenced deal, and more.

The comments table stores all comments made in response to the scraped posts. Titles in the 'title' column serve as foreign keys and link comments to the corresponding posts found in the main table.

Lastly, a cleaned version of the main table was supplied, for those who do not want to deal with data wrangling. The corresponding code can be found in the Kernel section.

Inspiration After data-wrangling of the main table, the set should be fairly simple to analyze and may contain some interesting deals. Since links to the sales are included, you may come across offerings that interest you.

The comments table can be used for natural language processing and more robust sentiment analysis. You may want to consider applying PCA.

Happy sales hunting!

Some questions you may want to answer:

Which users generate the most discussed posts or the highest number of upvotes? What type of products do top-users post? What products offer the biggest savings? What are the most popular product categories posted on the forum? Which retailers are most frequently represented? Which retailers generate the highest number of replies per pos

License

CC0

Original Data Source: Retail sale deals [crawled July 17, 2020]

China Online Retail Sales: YoY: Year to Date: Goods data was reported at 5.700 % in Mar 2025. This records an increase from the previous number of 5.000 % for Feb 2025. China Online Retail Sales: YoY: Year to Date: Goods data is updated monthly, averaging 19.900 % from Jun 2014 (Median) to Mar 2025, with 115 observations. The data reached an all-time high of 49.900 % in Sep 2014 and a record low of 3.000 % in Feb 2020. China Online Retail Sales: YoY: Year to Date: Goods data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Consumer Goods and Services – Table CN.HA: Online Retail Sales.

In 2019, online shopping transactions in S. Korea accounted for 21.4% of the total retail sales. As of November 2020, transaction value of online shopping (excluding services) grew by 17% compared to the same period of the past year, which accounted for 29% of the total retail sales in the country. KED Aicel’s S. Korea online commerce transaction data would help you identify the growth trend and driver of S. Korea online commerce market, as well as the performance of individual commerce companies. It would also allow you to observe changing dynamics of the user demographics such as gender and age in each online commerce company in the growing market. It leads this dataset to many uses; for example, investors can use this dataset to learn which customer base is driving each retailer’s revenue, while corporates can utilize this dataset to gauge consumer trend and help with their product and marketing strategies.

Singapore Retail Sales Value: Online Sales Proportion data was reported at 8.500 % in Mar 2020. This records an increase from the previous number of 7.500 % for Feb 2020. Singapore Retail Sales Value: Online Sales Proportion data is updated monthly, averaging 5.400 % from Jan 2018 (Median) to Mar 2020, with 27 observations. The data reached an all-time high of 8.500 % in Mar 2020 and a record low of 4.100 % in Feb 2018. Singapore Retail Sales Value: Online Sales Proportion data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Singapore – Table SG.H001: Retail Sales and Food & Beverage Services Value. [COVID-19-IMPACT]

Online Retail Sales: Multi Channel data was reported at 1,299.700 AUD mn in Mar 2020. This records an increase from the previous number of 1,073.800 AUD mn for Feb 2020. Online Retail Sales: Multi Channel data is updated monthly, averaging 526.200 AUD mn from Mar 2013 (Median) to Mar 2020, with 85 observations. The data reached an all-time high of 1,456.600 AUD mn in Dec 2019 and a record low of 271.300 AUD mn in Mar 2013. Online Retail Sales: Multi Channel data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.H015: Online Retail Sales. Multi-channel online retail trade comprises of retailers which combine an online store with a physical store and/or other non-traditional means such as catalogues, mail-order and/or telephone-order. [COVID-19-IMPACT]

This analysis applies a novel spatial mediation framework to examine how food retail accessibility mediates the relationship between deprivation and depression at the local level. The methodological approach combines mediation analysis principles (Judd, C.M. & Kenny, D.A., 1981) with Geographically Weighted Regression (GWR) models, allowing relationships to vary spatially across Hampshire and the Isle of Wight rather than assuming uniform effects across the region. The spatial mediation analysis involved two key steps: Step 1 established the total effect of income deprivation on depression, whilst Step 2 examined the indirect effect by modelling both deprivation and food retail accessibility as simultaneous predictors of depression. Local coefficients were then compared at each location to identify areas where food retail accessibility serves as a mediating pathway in the deprivation-depression relationship. Statistical significance was assessed using local t-values with a threshold of ±1.96 (p < 0.05), ensuring robust identification of meaningful mediation effects across different geographical contexts. The analysis utilised QOF depression prevalence data (2022), Index of Multiple Deprivation measures (2019), and Department for Transport travel time statistics to retail food outlets (2019), representing spatial access to food supply chain endpoints across the study region. Data sources: In all analyses, we used the LSOA boundaries published by the Office for National Statistics: Office for National Statistics. Census 2011 geographies [Internet]. 2020. Available from: Lower layer Super Output Areas (December 2011) https://geoportal.statistics.gov.uk/datasets/ons::lower-layer-super-output-areas-december-2011-boundaries-ew-bfc-v3/about Digital vector boundaries for Integrated Care Boards in England were those published by the Office for National Statistics: Integrated Care Boards (April 2023) EN BGC [Internet]. 2023. Available from: https://www.data.gov.uk/dataset/d6bcd7d1-0143-4366-9622-62a99b362a5c/integrated-care-boards-april-2023-en-bgc Depression Prevalence 2022 - QOF depression prevalence: Daras, K., Rose, T., Tsimpida, D., & Barr, B. (2023). Quality and Outcomes Framework Indicators: Depression prevalence (QOF_4_12) [Dataset]. University of Liverpool. Available from: https://datacat.liverpool.ac.uk/2170/ Retail accessibility: DfT. (2021). Journey time statistics, England: 2019 [Dataset]. Department for Transport. Available from: https://www.gov.uk/government/statistics/journey-time-statistics-england-2019/journey-time-statistics-england-2019#official-statistics Deprivation: McLennan, D., Noble, S., Noble, M., Plunkett, E., Wright, G., & Gutacker, N. (2019). The English indices of deprivation 2019: Technical report. Available from:https://www.gov.uk/government/statistics/english-indices-of-deprivation-2019 Longitudinal Depression: Tsimpida, D., Tsakiridi, A., Daras, K., Corcoran, R., & Gabbay, M. (2024). Unravelling the dynamics of mental health inequalities in England: A 12-year nationwide longitudinal spatial analysis of recorded depression prevalence. SSM - Population Health, 26, 101669. Available from: https://doi.org/10.1016/j.ssmph.2024.101669

Hong Kong SAR (China) Online Retail Sales Value Index: Non-store Retailing data was reported at 1,398.000 HKD mn in Mar 2025. This records an increase from the previous number of 1,272.000 HKD mn for Feb 2025. Hong Kong SAR (China) Online Retail Sales Value Index: Non-store Retailing data is updated monthly, averaging 1,075.000 HKD mn from Jan 2020 (Median) to Mar 2025, with 63 observations. The data reached an all-time high of 1,411.000 HKD mn in Nov 2024 and a record low of 647.000 HKD mn in Jan 2020. Hong Kong SAR (China) Online Retail Sales Value Index: Non-store Retailing data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.H: Online Retail Sales Value.

Poland Internet Retail Sales Index: Newspapers, Books, Other Sales in Specialized Stores data was reported at 110.000 Prev Mth=100 in Mar 2025. This records an increase from the previous number of 104.600 Prev Mth=100 for Feb 2025. Poland Internet Retail Sales Index: Newspapers, Books, Other Sales in Specialized Stores data is updated monthly, averaging 103.100 Prev Mth=100 from Jan 2020 (Median) to Mar 2025, with 63 observations. The data reached an all-time high of 145.500 Prev Mth=100 in Nov 2020 and a record low of 44.500 Prev Mth=100 in Jan 2025. Poland Internet Retail Sales Index: Newspapers, Books, Other Sales in Specialized Stores data remains active status in CEIC and is reported by Statistics Poland. The data is categorized under Global Database’s Poland – Table PL.H016: Internet Retail Sales Index: Previous Month=100.

Hong Kong SAR (China) Online Retail Sales Value Index: Other Retail Outlets data was reported at 1,027.000 HKD mn in Feb 2025. This records a decrease from the previous number of 1,072.000 HKD mn for Jan 2025. Hong Kong SAR (China) Online Retail Sales Value Index: Other Retail Outlets data is updated monthly, averaging 1,210.000 HKD mn from Jan 2020 (Median) to Feb 2025, with 62 observations. The data reached an all-time high of 3,010.000 HKD mn in Dec 2022 and a record low of 472.000 HKD mn in Feb 2020. Hong Kong SAR (China) Online Retail Sales Value Index: Other Retail Outlets data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.H019: Online Retail Sales Value.

Online retail in the United Kingdom has been gaining ground in the past decade. With the onset of the coronavirus (COVID-19) crisis, the value of online retail sales in the United Kingdom is estimated to reach just below *** billion British pounds in 2021. In 2022, the figure decreased to *** billion British pounds. What ranks high in UK e-commerce? In the United Kingdom, clothing and household goods were the most popular retail items consumers purchased through the internet in 2020. Data published by the Office for National Statistics (UK) showed that other leisure activities and services such as booking holiday accommodations, travel arrangements and event tickets were other areas consumers depended on the internet to buy. German e-commerce market The UK might have the highest share of online sales in retail trade, but other European countries such as Germany and France have had impressive track records over the years as well. According to the forecasts provided by German E-commerce and Distance Selling Trade Association (bevh), the market volume of Germany’s e-commerce sector was projected to see over ** billion euros in 2021.

Poland Internet Retail Sales Index data was reported at 115.200 Prev Mth=100 in Mar 2025. This records an increase from the previous number of 92.600 Prev Mth=100 for Feb 2025. Poland Internet Retail Sales Index data is updated monthly, averaging 100.800 Prev Mth=100 from Jan 2020 (Median) to Mar 2025, with 63 observations. The data reached an all-time high of 147.900 Prev Mth=100 in Nov 2020 and a record low of 67.300 Prev Mth=100 in Jan 2023. Poland Internet Retail Sales Index data remains active status in CEIC and is reported by Statistics Poland. The data is categorized under Global Database’s Poland – Table PL.H016: Internet Retail Sales Index: Previous Month=100.

United Kingdom RSI: SIC07: ISV: % of Total Retail: sa: Non Store Retailing data was reported at 82.400 % in Mar 2020. This records a decrease from the previous number of 83.600 % for Feb 2020. United Kingdom RSI: SIC07: ISV: % of Total Retail: sa: Non Store Retailing data is updated monthly, averaging 69.000 % from Jan 2008 (Median) to Mar 2020, with 147 observations. The data reached an all-time high of 83.600 % in Feb 2020 and a record low of 36.000 % in Jan 2008. United Kingdom RSI: SIC07: ISV: % of Total Retail: sa: Non Store Retailing data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.H014: Retail Sales Index: SIC 2007: Internet Sales Value as a Percent of Total Retail: Seasonally Adjusted. [COVID-19-IMPACT]

During the peak of the coronavirus (COVID-19) crisis (March-April 2020) when many countries worldwide introduced lockdown measures, e-commerce share in total retail sales saw proportions that were not seen before. In the United Kingdom, where an already mature e-commerce market exists, e-commerce share saw as high as **** percent, before stabilizing in the subsequent periods. In the most current period (as of January 31, 2021), United Kingdom, United States and Canada were the leading countries where e-commerce had a higher share as a proportion of total retail, at **, **, and ** percent, respectively.

RSI: SIC07: ISV: % of Total Retail: sa: NF: Household Goods Stores data was reported at 21.600 % in Mar 2020. This records an increase from the previous number of 14.400 % for Feb 2020. RSI: SIC07: ISV: % of Total Retail: sa: NF: Household Goods Stores data is updated monthly, averaging 6.200 % from Jan 2008 (Median) to Mar 2020, with 147 observations. The data reached an all-time high of 21.600 % in Mar 2020 and a record low of 3.100 % in Jan 2008. RSI: SIC07: ISV: % of Total Retail: sa: NF: Household Goods Stores data remains active status in CEIC and is reported by Office for National Statistics. The data is categorized under Global Database’s United Kingdom – Table UK.H014: Retail Sales Index: SIC 2007: Internet Sales Value as a Percent of Total Retail: Seasonally Adjusted. [COVID-19-IMPACT]